2019 BioEM, Montpellier, France
“Towards personalized treatment for TMS: Reducing the segmentation time“
Maria Tzirini, George Tsanidis, Ioannis Markakis, Yiftach Roth & Theodoros Samaras
Abstract
In order to apply personalized treatment for TMS, the segmentation time needs to be reduced. A method which uses fewer tissues models has been developed and its results have been validated with the use of three full-tissue high-resolution ViP models. The results are compared in terms of electric field distribution, induced by a Hesed coil in the cerebral tissues, and its maximum value. Moreover, the necessity of the dura mater design in the fewer tissue models is discussed.
2021 Frontiers in Network Physiology, Vol(1), https://doi.org/10.3389/fnetp.2021.706487
“Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG“
Koutlis Christos, Kimiskidis Vasilios, and Kugiumtzis Dimitris
Abstract
The usage of methods for the estimation of the true underlying connectivity among the observed variables of a system is increasing, especially in the domain of neuroscience. Granger causality and similar concepts are employed for the estimation of the brain network from electroencephalogram (EEG) data. Also source localization techniques, such as the standardized low resolution electromagnetic tomography (sLORETA), are widely used for obtaining more reliable data in the source space. In this work, connectivity structures are estimated in the sensor and in the source space making use of the sLORETA transformation for simulated and for EEG data with episodes of spontaneous epileptiform discharges (ED). From the comparative simulation study on high-dimensional coupled stochastic and deterministic systems originating in the sensor space, we conclude that the structure of the estimated causality networks differs in the sensor space and in the source space. Moreover, different network types, such as random, small-world and scale-free, can be better discriminated on the basis of the data in the original sensor space than on the transformed data in the source space. Similarly, in EEG epochs containing epileptiform discharges, the discriminative ability of network topological indices was significantly better in the sensor compared to the source level. In conclusion, causality networks constructed at the sensor and source level, for both simulated and empirical data, exhibit significant structural differences. These observations indicate that further studies are warranted in order to clarify the exact relationship between data registered in the sensor and source space.
2021 Conference on Complex Systems, Lyon, France
“TMS-induced brain connectivity modulation in Genetic Generalized Epilepsy“
Vlachos Ioannis, Kugiumtzis Dimitris, Kimiskidis Vasilios
Abstract
Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is an effective stimulation approach used for therapeutic and diagnostic purposes in neurological disorders [1]. In epilepsy patients, TMS may result in the induction and modulation of epileptiform discharges [2]. We hereby investigate the modulatory effects of TMS on brain connectivity in Genetic Generalized Epilepsy (GGE) and explore their potential as a diagnostic biomarker in GGE. Patients with GGE (n=18) and healthy controls (n=11) were investigated with a paired-pulse TMS-EEG protocol. The brain network was studied at local and at global levels using Coherence as an EEG connectivity measure. Comparison of patients vs controls was performed in a time-resolved manner by analyzing comparatively pre- vs post-TMS brain networks. There was statistically significant TMS-induced modulation of connectivity at specific frequency bands within groups and difference in TMS-induced modulation between the two groups. The most significant difference between patients and controls related to connectivity modulation in the γ band at 1-3 sec post-TMS (p=0.004) (see Fig.1). These findings suggest that: a) TMS modulates the healthy and epileptic brain connectivity in different ways, b) TMS-EEG connectivity analysis can be a basis for a diagnostic biomarker of epilepsy. The analysis identifies specific time periods and frequency bands of interest of TMS-induced connectivity modulation and elucidates the effect of TMS on the healthy and epileptic brain connectivity.
2022 Clinical Neurophysiology 133:83-93, https://doi.org/10.1016/j.clinph.2021.10.011.
“TMS-induced brain connectivity modulation in Genetic Generalized Epilepsy“
Vlachos Ioannis, Kugiumtzis Dimitris, Tsalikakis Dimitris and Kimiskidis Vasilios
Abstract
In epilepsy patients, Transcranial Magnetic Stimulation (TMS) may result in the induction and modulation of epileptiform discharges (EDs). We hereby investigate the modulatory effects of TMS on brain connectivity in Genetic Generalized Epilepsy (GGE) and explore their potential as a diagnostic biomarker in GGE.
2022 Applied Science 12(9):4509, https://doi.org/10.3390/app12094509
“Electric Field Distribution Induced by TMS: Differences Due to Anatomical Variation“
Tzirini Marietta, Evangelia Chatzikyriakou, Konstantinos Kouskouras, Nikolaos Foroglou, Theodoros Samaras, and Vasilios K. Kimiskidis
Abstract
Transcranial magnetic stimulation (TMS) is a well-established technique for the diagnosis and treatment of neuropsychiatric diseases. The numerical calculation of the induced electric field (EF) distribution in the brain increases the efficacy of stimulation and improves clinical outcomes. However, unique anatomical features, which distinguish each subject, suggest that personalized models should be preferentially used. The objective of the present study was to assess how anatomy affects the EF distribution and to determine to what extent personalized models are useful for clinical studies. The head models of nineteen healthy volunteers were automatically segmented. Two versions of each head model, a homogeneous and a five-tissue anatomical, were stimulated by the model of a Hesed coil (H-coil), employing magnetic quasi-static simulations. The H-coil was placed at two standard stimulating positions per model, over the frontal and central areas. The results show small, but indisputable, variations in the EFs for the homogeneous and anatomical models. The interquartile ranges in the anatomical versions were higher compared to the homogeneous ones, indicating that individual anatomical features may affect the prediction of stimulation volumes. It is concluded that personalized models provide complementary information and should be preferably employed in the context of diagnostic and therapeutic TMS studies.
2022 Applied Science 12:7437, https://doi.org/10.3390/app12157437
“The Relation between Induced Electric Field and TMS-Evoked Potentials: A Deep TMS-EEG Study“
Vlachos Ioannis, Tzirini Marietta, Evangelia Chatzikyriakou, Ioannis Markakis, Maria Anastasia Rouni, Theodoros Samaras, Yiftach Roth, Abraham Zangen, Alexander Rotenberg, Dimitris Kugiumtzis and Vasilios K. Kimiskidis
Abstract
Transcranial magnetic stimulation (TMS) in humans induces electric fields (E-fields, EF) that perturb and modulate the brain’s endogenous neuronal activity and result in the generation of TMS-evoked potentials (TEPs). The exact relation of the characteristics of the induced E-field and the intensity of the brains’ response, as measured by electroencephalography (EEG), is presently unclear. In this pilot study, conducted on three healthy subjects and two patients with generalized epilepsy (total: 3 males, 2 females, mean age of 26 years; healthy: 2 males, 1 female, mean age of 25.7 years; patients: 1 male, 1 female, mean age of 26.5 years), we investigated the temporal and spatial relations of the E-field, induced by single-pulse stimuli, and the brain’s response to TMS. Brain stimulation was performed with a deep TMS device (BrainsWay Ltd., Jerusalem, Israel) and an H7 coil placed over the central area. The induced EF was computed on personalized anatomical models of the subjects through magneto quasi-static simulations. We identified specific time instances and brain regions that exhibit high positive or negative associations of the E-field with brain activity. In
addition, we identified significant correlations of the brain’s response intensity with the strength of the induced E-field and finally prove that TEPs are better correlated with E-field characteristics than with the stimulator’s output. These observations provide further insight in the relation between Efield and the ensuing cortical activation, validate in a clinically relevant manner the results of Efield modeling and reinforce the view that personalized approaches should be adopted in the field of non-invasive brain stimulation.